Fix Iceberg read optimization returning NULLs for stats-less manifests (#1545)#1764
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il9ue wants to merge 1 commit intoAltinity:antalya-26.1from
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Fix Iceberg read optimization returning NULLs for stats-less manifests (#1545)#1764il9ue wants to merge 1 commit intoAltinity:antalya-26.1from
il9ue wants to merge 1 commit intoAltinity:antalya-26.1from
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When an Iceberg manifest's per-file column statistics are absent (a common case for non-Spark writers like pyiceberg with default settings), DataFileMetaInfo::columns_info is empty. The optimization in StorageObjectStorageSource::createReader misread this as 'all columns are absent from the file' and returned constant NULLs for every row while still returning the correct row count. Result: silent data loss on icebergLocal, icebergS3, icebergAzure, icebergHDFS, and all *Cluster variants. Track whether any per-file stats were emitted via a new 'any_stats_field_present' boolean on DataFileMetaInfo, populated during manifest parsing. The optimization's absent-NULL loop only fires when stats are present; when stats are absent entirely, fall through to the Parquet reader, which correctly handles both physically-present columns (read normally) and schema-evolved-absent columns (handled by IcebergMetadata::getInitialSchemaByPath setting the file's own schema as initial_header). Closes Altinity#1545.
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Fix Iceberg read optimization returning NULL for every column when reading from manifests written without per-file column statistics (typical of non-Spark writers like pyiceberg with default settings). Affects
icebergLocal,icebergS3,icebergAzure,icebergHDFS, and all*Clustervariants. Antalya 26.1 fix for Altinity/ClickHouse#1545.Description
Antalya-specific bug fix on
antalya-26.1(base tagv26.1.x.altinityantalya). No upstream cherry-pick — this bug exists only on Antalya, introduced by Altinity/ClickHouse#1069 ("Read optimization using Iceberg metadata"). Same fix is being applied toantalya-25.8andantalya-26.3as separate PRs.Why this fires
When reading an Iceberg table written by a non-Spark writer that omits per-file column statistics from the manifest's Avro schema (pyiceberg with default settings, format v1 writers, and others), the
allow_experimental_iceberg_read_optimizationpath produces silent data loss: correct row counts, every column valueNULL. The reporter confirmed it onicebergLocal; investigation showed the same code path fires foricebergS3,icebergAzure,icebergHDFS, and all*Clustervariants — anyone using a non-Spark Iceberg writer on any of these is silently returning NULLs.Root cause
IcebergIteratoralways populatesfile_meta_infobefore yielding objects, so thefile_meta_data.has_value()check in the optimization passes. The issue is what's inside the populatedDataFileMetaInfo: when the manifest'sdata_file.value_counts/column_sizes/null_value_countsAvro fields are all absent (per the Iceberg spec, all three are optional),DataFileMetaInfo::columns_infostays empty.The optimization's second loop in
StorageObjectStorageSource::createReaderthen iterates every requested column, finds none of them in the emptycolumns_infomap, and adds them all toconstant_columns_with_valueswithField()(NULL).requested_columns_copyis cleared,need_only_count = true, the Parquet reader returns row count only, andgenerate()injects every column as a constant-NULL column at the correct row count.The optimization conflates "no stats were written" with "all columns are absent." Absent stats tell us nothing about which columns are physically present in the file.
The fix
Add
any_stats_field_present(bool) toDataFileMetaInfo. Populate it inManifestFile.cpp—trueif any ofvalue_counts,column_sizes, ornull_value_countswere emitted by the writer. Gate the optimization's absent-NULL loop on this flag: when no stats were emitted, skip the loop entirely and fall through to the Parquet reader, which correctly handles both physically-present columns (read normally) and schema-evolved-absent columns (handled upstream byIcebergMetadata::getInitialSchemaByPathsetting the file's own schema asinitial_header).A per-column presence set was considered but is unnecessary because schema evolution is already handled upstream of the optimization; the boolean is sufficient.
JSON serialization (cluster reads via
toJson()/ JSON-ptr constructor) is updated to round-trip the new field. Missing-on-deserialization defaults tofalse, which matches pre-fix behavior.Files changed
src/Storages/ObjectStorage/DataLakes/IDataLakeMetadata.h: addedany_stats_field_presentfield toDataFileMetaInfo; constructor signature updated.src/Storages/ObjectStorage/DataLakes/IDataLakeMetadata.cpp: JSON serde round-trips the new field; missing-on-deserialize defaults tofalse.src/Storages/ObjectStorage/DataLakes/Iceberg/ManifestFile.h/.cpp: tracks whether any stats Avro field was present; passes the bool throughManifestFileEntrytoDataFileMetaInfo.src/Storages/ObjectStorage/DataLakes/Iceberg/IcebergIterator.cpp: forwards the new bool when constructingDataFileMetaInfo.src/Storages/ObjectStorage/StorageObjectStorageSource.cpp: the absent-NULL loop now skips whenany_stats_field_presentisfalse.Tested
ninja clickhouse, RelWithDebInfo, clang-21).test_iceberg_local_returns_actual_rows_with_stats_less_manifest— reproducer, fails without the fix, passes with it.test_iceberg_local_returns_correct_rows_when_optimization_disabled— control.test_iceberg_local_partial_stats_manifest_reads_correctly— manifest withvalue_countsonly.test_iceberg_local_full_stats_manifest_reads_correctly— full Spark-style stats regression guard.CI/CD Options
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